A Tree Species Classification Method Based on Multi-source Simultaneous High Resolution Remote Sensing Data

A high-resolution, remote sensing data technology, applied in instrumentation, computing, character and pattern recognition, etc., can solve the problems of refined tree species classification level and classification accuracy can not meet the needs of use, to improve classification accuracy, degree of automation and accuracy Enhanced, easy-to-promote results

Active Publication Date: 2018-11-20
NANJING FORESTRY UNIV
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Problems solved by technology

Apparently, these methods cannot meet the requirements for refining tree species classification levels and classification accuracy.

Method used

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  • A Tree Species Classification Method Based on Multi-source Simultaneous High Resolution Remote Sensing Data
  • A Tree Species Classification Method Based on Multi-source Simultaneous High Resolution Remote Sensing Data
  • A Tree Species Classification Method Based on Multi-source Simultaneous High Resolution Remote Sensing Data

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Embodiment 1

[0038] Overview of the test area

[0039] The research area is selected from the state-run Yushan Forest Farm in Changshu City, Jiangsu Province (120°42′9.4″E, 31°40′4.1″N), with an area of ​​about 1422hm2 , the elevation change range is 2-261m; the study area is located in a subtropical monsoon climate, with an average annual precipitation of 1062.5mm; the forest type belongs to subtropical secondary mixed forest, which can be subdivided into coniferous forest, broad-leaved forest and mixed forest. The main coniferous and broad-leaved deciduous tree species include Pinus massoniana, Quercus acutissima, Liquidambar formosan and Chestnut (Castanea mollissima), etc., and some evergreen broad-leaved tree species are also associated.

[0040] According to the tree species composition, age, and site stratification in Yushan Forest Farm’s forest resources investigation historical data (2012), seven 30m×30m square plots were selected, including three types of forests: coniferous fores...

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Abstract

The invention discloses a tree species classification method based on multi-source simultaneous high-resolution remote sensing data, using the high-resolution and hyperspectral data acquired by an integrated sensor at the same time, firstly based on the high-resolution data and an object-oriented segmentation method for crown width identification, Then based on the spatial details and spectral features extracted from the hyperspectral data and combined with the BP neural network classifier, the tree species is classified, and finally the accuracy is verified by the confusion matrix. The invention is based on a multi-scale segmentation algorithm based on edge detection, establishes segmentation levels of different scales from multiple layers and multiple patterns, performs segmentation and information extraction layer by layer, and improves the classification accuracy of subtropical natural secondary forest tree species and forest types.

Description

technical field [0001] The invention belongs to the technical fields of forestry investigation, dynamic monitoring and biological diversity, and in particular relates to a tree species classification method based on multi-source simultaneous high-resolution remote sensing data. Background technique [0002] Accurately obtaining forest tree species information and their spatial distribution is of great significance for understanding the structure, function and succession of forest ecosystems, as well as biodiversity. At the same time, information on the spatial distribution of tree species can be used to parameterize forest growth models and ecological process models, guiding and optimizing forest ecosystem simulations. Conventional tree species survey methods mainly rely on ground field surveys and manual interpretation of large-scale aerial photos, which usually consume a large amount of work and are not conducive to updating forest tree species information. The remote sen...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/188G06F18/24317
Inventor 曹林申鑫佘光辉
Owner NANJING FORESTRY UNIV
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